Memetic coral reefs optimization algorithms for optimal geometrical design of submerged arches

Pérez-Aracil, Jorge; Camacho-Gómez, Carlos; Hernández-Díaz, Alejandro Mateo; Pereira, Emiliano; Camacho, David; Salcedo‐Sanz, Sancho

doi:10.1016/j.swevo.2021.100958

Cited by 11 publications

(7 citation statements)

References 64 publications

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“…i Rprop + algorithm applies a backtracking strategy, and it decides whether to take a step back along a weight direction. Using this LS technique together with a CRO algorithm 28 would provide a memetic strategy for the design of ANNs.…”

Section: Introductionmentioning

confidence: 99%

A memetic dynamic coral reef optimisation algorithm for simultaneous training, design, and optimisation of artificial neural networks

Bérchez-Moreno,

Durán-Rosal,

Hervás Martínez

et al. 2024

Sci Rep

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Artificial Neural Networks (ANNs) have been used in a multitude of real-world applications given their predictive capabilities, and algorithms based on gradient descent, such as Backpropagation (BP) and variants, are usually considered for their optimisation. However, these algorithms have been shown to get stuck at local optima, and they require a cautious design of the architecture of the model. This paper proposes a novel memetic training method for simultaneously learning the ANNs structure and weights based on the Coral Reef Optimisation algorithms (CROs), a global-search metaheuristic based on corals’ biology and coral reef formation. Three versions based on the original CRO combined with a Local Search procedure are developed: (1) the basic one, called Memetic CRO; (2) a statistically guided version called Memetic SCRO (M-SCRO) that adjusts the algorithm parameters based on the population fitness; (3) and, finally, an improved Dynamic Statistically-driven version called Memetic Dynamic SCRO (M-DSCRO). M-DSCRO is designed with the idea of improving the M-SCRO version in the evolutionary process, evaluating whether the fitness distribution of the population of ANNs is normal to automatically decide the statistic to be used for assigning the algorithm parameters. Furthermore, all algorithms are adapted to the design of ANNs by means of the most suitable operators. The performance of the different algorithms is evaluated with 40 classification datasets, showing that the proposed M-DSCRO algorithm outperforms the other two versions on most of the datasets. In the final analysis, M-DSCRO is compared against four state-of-the-art methods, demonstrating its superior efficacy in terms of overall accuracy and minority class performance.

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Section: Introductionmentioning

confidence: 99%

A memetic dynamic coral reef optimisation algorithm for simultaneous training, design, and optimisation of artificial neural networks

Bérchez-Moreno,

Durán-Rosal,

Hervás Martínez

et al. 2024

Sci Rep

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“…where Equation ( 14) represents the strain compatibility, being ∆ε p and ε p the strain by imposition of the prestressing system and the strain of the prestressing strand, respectively, and Equation ( 15) represents the stress-strain relation for the prestressing steel, being f yp and E p its yield stress and elastic modulus, respectively. In summary, for a given value of tensile principal strain in concrete, ε 1 , where such strain works as an input parameter, the shear model for the prediction of the load-deformation behavior of a prestressed concrete beam is based on the nonlinear system of Equations (3)(4)(5)(6)(7)(8)(9)(10)(11), with to 10 equations (notice that Equation ( 13) is actually two equations in turn) in the 10 unknowns (θ, ε x , ε t , ν, ε 2 , σ 2 , σ s,x , σ s,t , ε p and σ p ).…”

Section: Problem Statementmentioning

confidence: 99%

“…The implementation of these models usually requires the application of numerical methods for solving the corresponding nonlinear equations, such as, for example, Newton-type methods [3]. In fact, the correct solver for solving a nonlinear problem is often a choice between computational cost and accuracy [4][5][6][7]. Moreover, in this work the previous determination of a solvability region using algebraic procedures is also necessary in order to improve the efficiency of the numerical solver, as indicated at Section 2.…”

Section: Introductionmentioning

confidence: 99%

A Class of Efficient Sixth-Order Iterative Methods for Solving the Nonlinear Shear Model of a Reinforced Concrete Beam

Padilla,

Chicharro,

Cordero

et al. 2024

Preprint

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In this paper, we present a three-step sixth-order iterative schemes to estimate the solutions of a nonlinear systems of equations, for predicting the shear strength of a reinforced concrete beam. This procedure is designed by means of a weight function technique. The values for the parameters of this system were randomly selected inside the prescribed ranges by technical standards for structural concrete; moreover, some of this parameters were fixed taking into consideration the solvability region of the adopted steel constitutive model. The efficiency of the new class is compared with other current schemes, with very good results.

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“…These models usually require in their implementation the application of iterative fixed-point methods to solve the nonlinear systems that appear. In fact, the most efficient way to solve a nonlinear problem is usually to choose between accuracy and computational cost [15]. Moreover, in this work, the previous determination of a solvability region using algebraic procedures is also necessary in order to improve the efficiency of the numerical solver, as indicated in Section 2.…”

Section: Introductionmentioning

confidence: 99%

A Class of Efficient Sixth-Order Iterative Methods for Solving the Nonlinear Shear Model of a Reinforced Concrete Beam

Padilla,

Chicharro,

Cordero

et al. 2024

Mathematics

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In this paper, we present a three-step sixth-order class of iterative schemes to estimate the solutions of a nonlinear system of equations. This procedure is designed by means of a weight function technique. We apply this procedure for predicting the shear strength of a reinforced concrete beam. The values for the parameters of the nonlinear system describing this problem were randomly selected inside the prescribed ranges by technical standards for structural concrete. Moreover, some of these parameters were fixed taking into consideration the solvability region of the adopted steel constitutive model. The effectiveness of the new class is also compared with other current schemes in terms of the computational efficiency and numerical performance, with very good results. The advantages of this new class come from the low computational cost, due to the existence of an only inverse operator.

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Memetic coral reefs optimization algorithms for optimal geometrical design of submerged arches

Cited by 11 publications

References 64 publications

A memetic dynamic coral reef optimisation algorithm for simultaneous training, design, and optimisation of artificial neural networks

A memetic dynamic coral reef optimisation algorithm for simultaneous training, design, and optimisation of artificial neural networks

A Class of Efficient Sixth-Order Iterative Methods for Solving the Nonlinear Shear Model of a Reinforced Concrete Beam

A Class of Efficient Sixth-Order Iterative Methods for Solving the Nonlinear Shear Model of a Reinforced Concrete Beam

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